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OAT-BLT-LIBERO-300

Name: OAT-BLT-LIBERO-300
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OAT Dense LIBERO-10 — Checkpoint Epoch 300

Hugging Face model repository for a dense cross-attention OAT policy trained on LIBERO-10 (N500). This snapshot was taken at epoch 300 during a long run (oat_dense_with_uid_long_0530_220204).

Files

File	Description
`ep-0300.ckpt`	PyTorch workspace checkpoint (~729 MB)
`training_logs.jsonl`	Full training JSONL (train/val curves)
`training_metrics_dashboard.png`	Training loss dashboard
`overfit_watcher/`	Counterfactual early-stop reports
`sim_eval/`	Phase A screen eval (30 ep/task)
`sim_eval_phase_b/`	Phase B confirm eval (50 ep/task, 3 exp) — ep-0700 only
`experiment_log_dense_visual_memory.md`	Experiment journal

Metrics @ epoch 300

Train loss	Val loss	Reconst MSE	Sim SR (Phase A)
2.4420902729034424	5.05718469619751	0.07346421480178833	39.7%

Training (offline)

Sim eval (LIBERO-10, Phase A screen)

Mean success rate: 39.7% — 30 episodes/task, 300 total rollouts, seed 1000. Details: sim_eval/eval_summary.md · sim_eval/eval_log.json

Model configuration (summary)

Policy: OAT with use_dense_visual_memory=true (spatial visual tokens + cross-attn)
State memory: enabled (use_state_memory_tokens=true)
Task UID: enabled in state tokens
Dataset: libero10_N500.zarr
Embed dim: 256

Baseline reference

Paper OAT8 on LIBERO-10: ~56.3% mean success rate (external reference).

Citation

If you use this checkpoint, please cite OAT: Ordered Action Tokenization and specify epoch 300 of the dense LIBERO-10 ladder.

hackhackhack66666/OAT-BLT-LIBERO-300